An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm
Abstract Blood cell count is an important parameter in analysing a person’s health condition. White blood corpuscles (leukocytes) are responsible for deciding the immunity system of a person. White blood cells (WBC) are classified into: lymphocytes, monocytes and granulocytes. Granulocytes are sub-c...
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Gespeichert in:
| Autor*in: |
Balasubramanian, Kishore [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 34(2022), 18 vom: 03. Mai, Seite 16089-16101 |
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| Übergeordnetes Werk: |
volume:34 ; year:2022 ; number:18 ; day:03 ; month:05 ; pages:16089-16101 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00521-022-07279-1 |
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OLC207940783X |
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| 520 | |a Abstract Blood cell count is an important parameter in analysing a person’s health condition. White blood corpuscles (leukocytes) are responsible for deciding the immunity system of a person. White blood cells (WBC) are classified into: lymphocytes, monocytes and granulocytes. Granulocytes are sub-classified into: neutrophils, eosinophils, and basophils. These five types perform different functions in acting as a defence mechanism of the body. Manual investigation performed at laboratories for WBC count is prone to errors due to certain factors like human fatigue, inter-operability errors, etc. Further, there is serious issue in using trained data which cater to the changes in morphology of the white blood corpuscles, in order that trained classifiers could capitalize well. In a way to reduce misclassification rate, a methodology wherein a deep learning approach integrated with an evolutionary algorithm is proposed. Convolutional neural network (CNN) hyper-parameters were optimized using particle swarm optimization algorithm (PSO) to improve the network performance in classifying white blood cells into five types. The method is tested on merged LISC and BCCD datasets which achieved classification accuracy of 99.2% with 94.56% sensitivity, 98.78% specificity and 0.982 AUC. The results are compared with similar proven algorithms like genetic algorithms (GA), differential evolution (DE) and grey wolf optimization (GWO) algorithms. The experimental outcomes demonstrated PSO’s potential in optimizing the CNN hyper-parameters for white blood cell classification enhancing the sensitivity rate and serve a best second opinion in assessing blood cell count. | ||
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10.1007/s00521-022-07279-1 doi (DE-627)OLC207940783X (DE-He213)s00521-022-07279-1-p DE-627 ger DE-627 rakwb eng 004 VZ Balasubramanian, Kishore verfasserin (orcid)0000-0003-1918-9774 aut An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Blood cell count is an important parameter in analysing a person’s health condition. White blood corpuscles (leukocytes) are responsible for deciding the immunity system of a person. White blood cells (WBC) are classified into: lymphocytes, monocytes and granulocytes. Granulocytes are sub-classified into: neutrophils, eosinophils, and basophils. These five types perform different functions in acting as a defence mechanism of the body. Manual investigation performed at laboratories for WBC count is prone to errors due to certain factors like human fatigue, inter-operability errors, etc. Further, there is serious issue in using trained data which cater to the changes in morphology of the white blood corpuscles, in order that trained classifiers could capitalize well. In a way to reduce misclassification rate, a methodology wherein a deep learning approach integrated with an evolutionary algorithm is proposed. Convolutional neural network (CNN) hyper-parameters were optimized using particle swarm optimization algorithm (PSO) to improve the network performance in classifying white blood cells into five types. The method is tested on merged LISC and BCCD datasets which achieved classification accuracy of 99.2% with 94.56% sensitivity, 98.78% specificity and 0.982 AUC. The results are compared with similar proven algorithms like genetic algorithms (GA), differential evolution (DE) and grey wolf optimization (GWO) algorithms. The experimental outcomes demonstrated PSO’s potential in optimizing the CNN hyper-parameters for white blood cell classification enhancing the sensitivity rate and serve a best second opinion in assessing blood cell count. Leukocytes Blood Classification PSO CNN Ananthamoorthy, N. P. aut Ramya, K. aut Enthalten in Neural computing & applications Springer London, 1993 34(2022), 18 vom: 03. Mai, Seite 16089-16101 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2022 number:18 day:03 month:05 pages:16089-16101 https://doi.org/10.1007/s00521-022-07279-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2022 18 03 05 16089-16101 |
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10.1007/s00521-022-07279-1 doi (DE-627)OLC207940783X (DE-He213)s00521-022-07279-1-p DE-627 ger DE-627 rakwb eng 004 VZ Balasubramanian, Kishore verfasserin (orcid)0000-0003-1918-9774 aut An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Blood cell count is an important parameter in analysing a person’s health condition. White blood corpuscles (leukocytes) are responsible for deciding the immunity system of a person. White blood cells (WBC) are classified into: lymphocytes, monocytes and granulocytes. Granulocytes are sub-classified into: neutrophils, eosinophils, and basophils. These five types perform different functions in acting as a defence mechanism of the body. Manual investigation performed at laboratories for WBC count is prone to errors due to certain factors like human fatigue, inter-operability errors, etc. Further, there is serious issue in using trained data which cater to the changes in morphology of the white blood corpuscles, in order that trained classifiers could capitalize well. In a way to reduce misclassification rate, a methodology wherein a deep learning approach integrated with an evolutionary algorithm is proposed. Convolutional neural network (CNN) hyper-parameters were optimized using particle swarm optimization algorithm (PSO) to improve the network performance in classifying white blood cells into five types. The method is tested on merged LISC and BCCD datasets which achieved classification accuracy of 99.2% with 94.56% sensitivity, 98.78% specificity and 0.982 AUC. The results are compared with similar proven algorithms like genetic algorithms (GA), differential evolution (DE) and grey wolf optimization (GWO) algorithms. The experimental outcomes demonstrated PSO’s potential in optimizing the CNN hyper-parameters for white blood cell classification enhancing the sensitivity rate and serve a best second opinion in assessing blood cell count. Leukocytes Blood Classification PSO CNN Ananthamoorthy, N. P. aut Ramya, K. aut Enthalten in Neural computing & applications Springer London, 1993 34(2022), 18 vom: 03. Mai, Seite 16089-16101 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2022 number:18 day:03 month:05 pages:16089-16101 https://doi.org/10.1007/s00521-022-07279-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2022 18 03 05 16089-16101 |
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10.1007/s00521-022-07279-1 doi (DE-627)OLC207940783X (DE-He213)s00521-022-07279-1-p DE-627 ger DE-627 rakwb eng 004 VZ Balasubramanian, Kishore verfasserin (orcid)0000-0003-1918-9774 aut An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Blood cell count is an important parameter in analysing a person’s health condition. White blood corpuscles (leukocytes) are responsible for deciding the immunity system of a person. White blood cells (WBC) are classified into: lymphocytes, monocytes and granulocytes. Granulocytes are sub-classified into: neutrophils, eosinophils, and basophils. These five types perform different functions in acting as a defence mechanism of the body. Manual investigation performed at laboratories for WBC count is prone to errors due to certain factors like human fatigue, inter-operability errors, etc. Further, there is serious issue in using trained data which cater to the changes in morphology of the white blood corpuscles, in order that trained classifiers could capitalize well. In a way to reduce misclassification rate, a methodology wherein a deep learning approach integrated with an evolutionary algorithm is proposed. Convolutional neural network (CNN) hyper-parameters were optimized using particle swarm optimization algorithm (PSO) to improve the network performance in classifying white blood cells into five types. The method is tested on merged LISC and BCCD datasets which achieved classification accuracy of 99.2% with 94.56% sensitivity, 98.78% specificity and 0.982 AUC. The results are compared with similar proven algorithms like genetic algorithms (GA), differential evolution (DE) and grey wolf optimization (GWO) algorithms. The experimental outcomes demonstrated PSO’s potential in optimizing the CNN hyper-parameters for white blood cell classification enhancing the sensitivity rate and serve a best second opinion in assessing blood cell count. Leukocytes Blood Classification PSO CNN Ananthamoorthy, N. P. aut Ramya, K. aut Enthalten in Neural computing & applications Springer London, 1993 34(2022), 18 vom: 03. Mai, Seite 16089-16101 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2022 number:18 day:03 month:05 pages:16089-16101 https://doi.org/10.1007/s00521-022-07279-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2022 18 03 05 16089-16101 |
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10.1007/s00521-022-07279-1 doi (DE-627)OLC207940783X (DE-He213)s00521-022-07279-1-p DE-627 ger DE-627 rakwb eng 004 VZ Balasubramanian, Kishore verfasserin (orcid)0000-0003-1918-9774 aut An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Blood cell count is an important parameter in analysing a person’s health condition. White blood corpuscles (leukocytes) are responsible for deciding the immunity system of a person. White blood cells (WBC) are classified into: lymphocytes, monocytes and granulocytes. Granulocytes are sub-classified into: neutrophils, eosinophils, and basophils. These five types perform different functions in acting as a defence mechanism of the body. Manual investigation performed at laboratories for WBC count is prone to errors due to certain factors like human fatigue, inter-operability errors, etc. Further, there is serious issue in using trained data which cater to the changes in morphology of the white blood corpuscles, in order that trained classifiers could capitalize well. In a way to reduce misclassification rate, a methodology wherein a deep learning approach integrated with an evolutionary algorithm is proposed. Convolutional neural network (CNN) hyper-parameters were optimized using particle swarm optimization algorithm (PSO) to improve the network performance in classifying white blood cells into five types. The method is tested on merged LISC and BCCD datasets which achieved classification accuracy of 99.2% with 94.56% sensitivity, 98.78% specificity and 0.982 AUC. The results are compared with similar proven algorithms like genetic algorithms (GA), differential evolution (DE) and grey wolf optimization (GWO) algorithms. The experimental outcomes demonstrated PSO’s potential in optimizing the CNN hyper-parameters for white blood cell classification enhancing the sensitivity rate and serve a best second opinion in assessing blood cell count. Leukocytes Blood Classification PSO CNN Ananthamoorthy, N. P. aut Ramya, K. aut Enthalten in Neural computing & applications Springer London, 1993 34(2022), 18 vom: 03. Mai, Seite 16089-16101 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2022 number:18 day:03 month:05 pages:16089-16101 https://doi.org/10.1007/s00521-022-07279-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2022 18 03 05 16089-16101 |
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10.1007/s00521-022-07279-1 doi (DE-627)OLC207940783X (DE-He213)s00521-022-07279-1-p DE-627 ger DE-627 rakwb eng 004 VZ Balasubramanian, Kishore verfasserin (orcid)0000-0003-1918-9774 aut An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract Blood cell count is an important parameter in analysing a person’s health condition. White blood corpuscles (leukocytes) are responsible for deciding the immunity system of a person. White blood cells (WBC) are classified into: lymphocytes, monocytes and granulocytes. Granulocytes are sub-classified into: neutrophils, eosinophils, and basophils. These five types perform different functions in acting as a defence mechanism of the body. Manual investigation performed at laboratories for WBC count is prone to errors due to certain factors like human fatigue, inter-operability errors, etc. Further, there is serious issue in using trained data which cater to the changes in morphology of the white blood corpuscles, in order that trained classifiers could capitalize well. In a way to reduce misclassification rate, a methodology wherein a deep learning approach integrated with an evolutionary algorithm is proposed. Convolutional neural network (CNN) hyper-parameters were optimized using particle swarm optimization algorithm (PSO) to improve the network performance in classifying white blood cells into five types. The method is tested on merged LISC and BCCD datasets which achieved classification accuracy of 99.2% with 94.56% sensitivity, 98.78% specificity and 0.982 AUC. The results are compared with similar proven algorithms like genetic algorithms (GA), differential evolution (DE) and grey wolf optimization (GWO) algorithms. The experimental outcomes demonstrated PSO’s potential in optimizing the CNN hyper-parameters for white blood cell classification enhancing the sensitivity rate and serve a best second opinion in assessing blood cell count. Leukocytes Blood Classification PSO CNN Ananthamoorthy, N. P. aut Ramya, K. aut Enthalten in Neural computing & applications Springer London, 1993 34(2022), 18 vom: 03. Mai, Seite 16089-16101 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:34 year:2022 number:18 day:03 month:05 pages:16089-16101 https://doi.org/10.1007/s00521-022-07279-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_2018 GBV_ILN_4277 AR 34 2022 18 03 05 16089-16101 |
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Balasubramanian, Kishore Ananthamoorthy, N. P. Ramya, K. |
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Balasubramanian, Kishore |
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an approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm |
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An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm |
| abstract |
Abstract Blood cell count is an important parameter in analysing a person’s health condition. White blood corpuscles (leukocytes) are responsible for deciding the immunity system of a person. White blood cells (WBC) are classified into: lymphocytes, monocytes and granulocytes. Granulocytes are sub-classified into: neutrophils, eosinophils, and basophils. These five types perform different functions in acting as a defence mechanism of the body. Manual investigation performed at laboratories for WBC count is prone to errors due to certain factors like human fatigue, inter-operability errors, etc. Further, there is serious issue in using trained data which cater to the changes in morphology of the white blood corpuscles, in order that trained classifiers could capitalize well. In a way to reduce misclassification rate, a methodology wherein a deep learning approach integrated with an evolutionary algorithm is proposed. Convolutional neural network (CNN) hyper-parameters were optimized using particle swarm optimization algorithm (PSO) to improve the network performance in classifying white blood cells into five types. The method is tested on merged LISC and BCCD datasets which achieved classification accuracy of 99.2% with 94.56% sensitivity, 98.78% specificity and 0.982 AUC. The results are compared with similar proven algorithms like genetic algorithms (GA), differential evolution (DE) and grey wolf optimization (GWO) algorithms. The experimental outcomes demonstrated PSO’s potential in optimizing the CNN hyper-parameters for white blood cell classification enhancing the sensitivity rate and serve a best second opinion in assessing blood cell count. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
| abstractGer |
Abstract Blood cell count is an important parameter in analysing a person’s health condition. White blood corpuscles (leukocytes) are responsible for deciding the immunity system of a person. White blood cells (WBC) are classified into: lymphocytes, monocytes and granulocytes. Granulocytes are sub-classified into: neutrophils, eosinophils, and basophils. These five types perform different functions in acting as a defence mechanism of the body. Manual investigation performed at laboratories for WBC count is prone to errors due to certain factors like human fatigue, inter-operability errors, etc. Further, there is serious issue in using trained data which cater to the changes in morphology of the white blood corpuscles, in order that trained classifiers could capitalize well. In a way to reduce misclassification rate, a methodology wherein a deep learning approach integrated with an evolutionary algorithm is proposed. Convolutional neural network (CNN) hyper-parameters were optimized using particle swarm optimization algorithm (PSO) to improve the network performance in classifying white blood cells into five types. The method is tested on merged LISC and BCCD datasets which achieved classification accuracy of 99.2% with 94.56% sensitivity, 98.78% specificity and 0.982 AUC. The results are compared with similar proven algorithms like genetic algorithms (GA), differential evolution (DE) and grey wolf optimization (GWO) algorithms. The experimental outcomes demonstrated PSO’s potential in optimizing the CNN hyper-parameters for white blood cell classification enhancing the sensitivity rate and serve a best second opinion in assessing blood cell count. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract Blood cell count is an important parameter in analysing a person’s health condition. White blood corpuscles (leukocytes) are responsible for deciding the immunity system of a person. White blood cells (WBC) are classified into: lymphocytes, monocytes and granulocytes. Granulocytes are sub-classified into: neutrophils, eosinophils, and basophils. These five types perform different functions in acting as a defence mechanism of the body. Manual investigation performed at laboratories for WBC count is prone to errors due to certain factors like human fatigue, inter-operability errors, etc. Further, there is serious issue in using trained data which cater to the changes in morphology of the white blood corpuscles, in order that trained classifiers could capitalize well. In a way to reduce misclassification rate, a methodology wherein a deep learning approach integrated with an evolutionary algorithm is proposed. Convolutional neural network (CNN) hyper-parameters were optimized using particle swarm optimization algorithm (PSO) to improve the network performance in classifying white blood cells into five types. The method is tested on merged LISC and BCCD datasets which achieved classification accuracy of 99.2% with 94.56% sensitivity, 98.78% specificity and 0.982 AUC. The results are compared with similar proven algorithms like genetic algorithms (GA), differential evolution (DE) and grey wolf optimization (GWO) algorithms. The experimental outcomes demonstrated PSO’s potential in optimizing the CNN hyper-parameters for white blood cell classification enhancing the sensitivity rate and serve a best second opinion in assessing blood cell count. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm |
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https://doi.org/10.1007/s00521-022-07279-1 |
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